Knowledge Graphs (KGs) have emerged as a powerful tool for representing semantic relationships between data entities and are widely used in various applications such as search engines, information retrieval, and natural language processing. This study provides an extensive literature review of Named Entity Extraction (NEE), focusing on recent advances in Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). The paper highlights how these processes facilitate the transformation of unstructured natural language data into structured knowledge suitable for KGs. Additionally, it explores the evolution of approaches from rule-based systems to machine learning and neural network-based methods, emphasizing their impact on accuracy and efficiency. The findings suggest that while NER, NED, and NEL are critical for semantic lifting, challenges remain, particularly in handling ambiguous entities and integrating off-the-shelf NER tools with domain-specific knowledge graphs.